AE 08: Data wrangling with rowwise/column-wise operations

Note

Some chunks require you to merely fill in the TODOs with the appropriate code. Other chunks require you to write the entire code block.

Packages

We will use the following packages in this application exercise.

• tidyverse: For data import, wrangling, and visualization.
• janitor: For cleaning column names.

library(tidyverse)
library(janitor)
library(scales)

Powerball

Last class we studied Powerball jackpots over time. Today we will continue this journey and focus on Colorado winners in Match \(N\) Powerball play, prizes available to players who match the red Powerball number and anywhere between 0-4 white ball numbers.

Import and clean the data

The dataset is available for download as a CSV file.

Demo: Import the data file. Store it as powerball_raw.

powerball_raw <- read_csv(file = "data/POWERBALL-from_0001-01-01_to_2024-02-07.csv")
powerball_raw

Your turn: Clean the raw data to fix the following issues:

• Standardize the column names using snake_case format.
• Create columns with appropriate data types for any date variables date of the drawing as well as the weekday. Append these columns to the beginning of the data frame.
• Fix all of the currency columns to be formatted as numeric types.

Store the cleaned data frame as powerball.

powerball <- powerball_raw |>
  clean_names() |>
  # separate draw_date into two variables, clean both
  separate_wider_delim(
    cols = draw_date,
    delim = ",",
    names = c(NA, "draw_date")
  ) |>
  mutate(
    draw_date = mdy(draw_date),
    last_day_to_claim = mdy(last_day_to_claim),
    draw_weekday = wday(x = draw_date, label = TRUE),
    .before = last_day_to_claim
  ) |>
  # convert all currency columns to numeric type
  mutate(
    across(
      .cols = TODO,
      .fn = parse_number
    )
  )

Analyze the data

Our goal is to reproduce the following visualization:

In order to accomplish this, we have a few challenges ahead. We will need to:

• Determine the year for every drawing
• Calculate the mean and standard error of the number of winners for each Match \(N\) + Powerball prize for every year
• Structure the data frame so we have one row for each year and prize, and separate columns for the means and standard errors

Generate the year variable

Your turn: Generate a year variable from the draw_date column.

# add code here
powerball |>
  # generate year variable
  mutate(
    year = TODO,
    .before = everything()
  )

Calculate means and standard errors

Your turn: Calculate the mean and standard error for each of the Match \(N\) + Powerball prizes for each year. Your data frame should look something like this:

# A tibble: 23 × 11
    year match_4_powerball_co_winners_m…¹ match_4_powerball_co…² match_3_powerball_co…³ match_3_powerball_co…⁴ match_2_powerball_co…⁵
   <dbl>                            <dbl>                  <dbl>                  <dbl>                  <dbl>                  <dbl>
 1  2023                           0.193                  0.0496                   9.75                  1.08                    198.
 2  2022                           0.153                  0.0363                  10.1                   1.66                    205.
 3  2021                           0.138                  0.0312                   9.88                  0.804                   198.
 4  2020                           0.0762                 0.0260                   7.49                  0.481                   156.
 5  2019                           0.125                  0.0326                  10.8                   1.18                    212.
 6  2018                           0.221                  0.0510                  11.3                   1.04                    236.
 7  2017                           0.192                  0.0412                  13.4                   1.89                    270.
 8  2016                           0.295                  0.103                   19.2                   5.47                    386.
 9  2015                           0.288                  0.0839                  12.9                   1.55                    233.
10  2014                           0.248                  0.0484                  13.1                   0.764                   218.
# ℹ 13 more rows
# ℹ abbreviated names: ¹​match_4_powerball_co_winners_mean, ²​match_4_powerball_co_winners_se, ³​match_3_powerball_co_winners_mean,
#   ⁴​match_3_powerball_co_winners_se, ⁵​match_2_powerball_co_winners_mean
# ℹ 5 more variables: match_2_powerball_co_winners_se <dbl>, match_1_powerball_co_winners_mean <dbl>,
#   match_1_powerball_co_winners_se <dbl>, match_0_powerball_co_winners_mean <dbl>, match_0_powerball_co_winners_se <dbl>
# ℹ Use `print(n = ...)` to see more rows

Tip

Recall the formula for the standard error of a sample mean is:

\[ \begin{aligned} \text{s.e.} &= \sqrt{\frac{\text{Variance}(X)}{\text{Sample size}}} \\ &= \frac{\text{Standard deviation}(X)}{\sqrt{\text{Sample size}}} \end{aligned} \]

# add code here
powerball |>
  # generate year variable
  mutate(
    year = TODO,
    .before = everything()
  ) |>
  # calculate mean and se for the match N powerball winner columns
  summarize(
    across(
      .cols = TODO,
      .fns = list(mean = mean, se = TODO)
    ),
    # do this for each year in the dataset
    .by = year
  )

Clean up column names

Your turn: Remove "powerball_co_winners_" from each column name.

Tip

rename() does not allow use of the across() function. Instead, check out rename_with() from the dplyr package.

Tip

stringr contains many functions for working with character strings. Check out the cheat sheet for examples!

# add code here
powerball |>
  # generate year variable
  mutate(
    year = TODO,
    .before = everything()
  ) |>
  # calculate mean and se for the match N powerball winner columns
  summarize(
    across(
      .cols = TODO,
      .fns = list(mean = mean, se = TODO)
    ),
    # do this for each year in the dataset
    .by = year
  ) |>
  # remove extraneous string from columns
  rename_with(
    .cols = TODO,
    .fn = \(x) str_TODO(string = x, pattern = "powerball_co_winners_")
  )

Restructure data frame to appropriate form for visualization

Demo: We need the structure to be one row for each year and prize (i.e. 0, 1, 2, 3, 4) and separate columns for the means and standard errors. We can use pivot_longer() to accomplish this task, but it’s a bit more complicated than past pivoting operations since the column names contain both a variable (e.g. match_4) and a variable name (i.e. mean or se).

# add code here
powerball_stats <- powerball |>
  # generate year variable
  mutate(
    year = TODO,
    .before = everything()
  ) |>
  # calculate mean and se for the match N powerball winner columns
  summarize(
    across(
      .cols = TODO,
      .fns = list(mean = mean, se = TODO)
    ),
    # do this for each year in the dataset
    .by = year
  ) |>
  # remove extraneous string from columns
  rename_with(
    .cols = TODO,
    .fn = \(x) str_TODO(string = x, pattern = "powerball_co_winners_")
  ) |>
  # restructure to one row per year per game
  # separate columns for mean and se
  pivot_longer(
    cols = -year,
    # columns contain a variable and a variable name
    names_to = c("game", ".value"),
    # ignore column prefix
    names_prefix = "match_",
    # separating character
    names_sep = "_"
  ) |>
  # reformat game column values for visualization
  mutate(game = str_glue("Match {game}"))
powerball_stats

Plot the data

Demo: Now that we have the appropriate data structure, we can create the visualization.

ggplot(data = powerball_stats, mapping = aes(x = year, y = mean)) +
  geom_point() +
  geom_linerange(mapping = aes(
    ymin = mean - se,
    ymax = mean + se
  )) +
  facet_wrap(facets = vars(game), scales = "free_y") +
  labs(
    title = "The number of Match N Powerball Prize winners trends downward",
    subtitle = "Average number of prize winners (plus/minus 1 standard error)",
    x = "Year",
    y = "Number of Colorado winners",
    caption = "Source: Colorado Lottery"
  ) +
  theme_minimal()